Enterprises have recognized the potential of generative AI to drive innovation and improve productivity. However, there are significant risks associated with hosting sensitive and proprietary data in publicly hosted large language models (LLMs). Security, privacy, and governance concerns are top priorities for businesses. But how can companies extract value from LLMs while mitigating these risks?
Bringing LLMs to Your Data
Instead of sending data to an LLM, the preferred approach for most enterprises is to bring the LLM to their data. This allows businesses to balance innovation with the need for data security. By hosting and deploying LLMs within their existing security perimeter, companies can develop and customize the models while ensuring the protection of customer data and maintaining governance.
A Strong Data Strategy for AI Success
To create a strong AI strategy, a solid foundation in data is essential. Enterprises need to eliminate data silos and establish consistent policies for data access within a secure and governed environment. The goal is to have actionable and reliable data that can be easily utilized with an LLM. By leveraging their own data and extending and customizing existing models, businesses can make LLMs smarter and more relevant to their specific needs.
The Pitfalls of Large-Scale LLMs
LLMs trained on the entire web present challenges beyond privacy concerns. They can create inaccurate and biased responses, as well as generate offensive content, which poses significant risks for businesses. Additionally, foundational LLMs lack exposure to an organization’s internal systems and data, limiting their ability to answer questions specific to the business, customers, and industry.
Customizing Models for Your Business
To address these challenges, enterprises can download and customize LLMs behind their own firewall. Open-source models, such as StarCoder from Hugging Face and StableLM from Stability AI, offer the flexibility to tune and tailor models according to business needs. Tuning a model trained on the entire web requires vast amounts of data and computing power. However, once a model is trained, it can be fine-tuned for specific content domains with less data.
An effective LLM doesn’t need to be overwhelmingly large. Customizing models using internal data that employees can trust and that provide the necessary insights is crucial. Instead of relying on a broad range of information, businesses should focus on specific use cases that drive value. By fine-tuning LLMs to target these use cases, enterprises can achieve higher-quality results while reducing resource requirements.
Accessing valuable information to train LLMs often requires extracting data from unstructured sources such as emails, images, contracts, and training videos. Natural language processing and other technologies play a vital role in extracting and processing this unstructured data, enabling businesses to build and train multimodal AI models. These models can identify relationships between different data types and generate insights specific to the organization’s needs.
Businesses must exercise caution and due diligence when adopting generative AI. It is crucial to carefully review the models and services used and work with reputable vendors that provide explicit guarantees. However, companies cannot afford to stand still in this rapidly evolving field. Every business should explore how AI can disrupt its industry. By bringing generative AI models close to their data and operating within existing security perimeters, enterprises can strike the right balance between risk and reward, maximizing the opportunities offered by this new technology.
Generative AI has immense potential, but it comes with inherent risks. Enterprises must prioritize security, privacy, and governance when leveraging large language models. By bringing LLMs to their data, businesses can maintain control over sensitive information while harnessing the power of AI. A strong data strategy, customized models, and the utilization of unstructured data can optimize LLMs for specific use cases, resulting in higher-quality results and cost-effective operations. While caution is necessary, exploring and adopting AI technologies is essential for business growth and disruption. Striking the right balance between risk and reward will allow enterprises to unlock the full potential of generative AI.
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